Machine Learning for neutron source distributions
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866911675243823104 |
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| author | Robledo, Jose Ignacio Schmidt, Norberto Lieutenant, Klaus Li, Jingjing Kesselheim, Stefan Zakalek, Paul |
| author_facet | Robledo, Jose Ignacio Schmidt, Norberto Lieutenant, Klaus Li, Jingjing Kesselheim, Stefan Zakalek, Paul |
| contents | In light of the recent advancements in machine learning, we propose a novel approach to neutron source distribution estimation through the utilisation of probabilistic generative models. The estimation is based on a Monte Carlo particle list, which is only required during the training stage of the machine learning model. Once the source distribution has been learned, the model is independent of the original particle list, allowing for further sampling in an efficient, rapid, and memory-costless manner. The performance of various generative models is evaluated, including a variational autoencoder, a normalizing flow, a generative adversarial network, and a denoising diffusion model. These approaches are then compared to existing source distribution estimations, and the advantages and disadvantages of each approach are discussed. The results demonstrate that source distributions can be modeled through the use of probabilistic generative models, which paves the way for further advancements in this field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_12165 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Machine Learning for neutron source distributions Robledo, Jose Ignacio Schmidt, Norberto Lieutenant, Klaus Li, Jingjing Kesselheim, Stefan Zakalek, Paul Instrumentation and Detectors Machine Learning Computational Physics In light of the recent advancements in machine learning, we propose a novel approach to neutron source distribution estimation through the utilisation of probabilistic generative models. The estimation is based on a Monte Carlo particle list, which is only required during the training stage of the machine learning model. Once the source distribution has been learned, the model is independent of the original particle list, allowing for further sampling in an efficient, rapid, and memory-costless manner. The performance of various generative models is evaluated, including a variational autoencoder, a normalizing flow, a generative adversarial network, and a denoising diffusion model. These approaches are then compared to existing source distribution estimations, and the advantages and disadvantages of each approach are discussed. The results demonstrate that source distributions can be modeled through the use of probabilistic generative models, which paves the way for further advancements in this field. |
| title | Machine Learning for neutron source distributions |
| topic | Instrumentation and Detectors Machine Learning Computational Physics |
| url | https://arxiv.org/abs/2605.12165 |